Intelligent proactive responses to operations to read data from memory cells

ABSTRACT

A memory device to generate intelligent, proactive responses to a read command. For example, signal and noise characteristics of a group of memory cells in a memory device are measured to determine a read voltage. An action is identified based on evaluation of the quality of data retrievable using the read voltage from the group of memory cells. While a response indicating the action is provided responsive to the command, the memory device can initiate the action proactively before a subsequent command, following the response, is received.

RELATED APPLICATIONS

The present application is a continuation application of U.S. patentapplication Ser. No. 16/869,494, filed May 7, 2020 and entitled“Intelligent Proactive Responses to Operations to Read Data from MemoryCells,” the entire disclosure of which application is herebyincorporated herein by reference.

FIELD OF THE TECHNOLOGY

At least some embodiments disclosed herein relate to memory systems ingeneral, and more particularly, but not limited to intelligent proactiveresponses to read operations in memory systems.

BACKGROUND

A memory sub-system can include one or more memory devices that storedata. The memory devices can be, for example, non-volatile memorydevices and volatile memory devices. In general, a host system canutilize a memory sub-system to store data at the memory devices and toretrieve data from the memory devices.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments are illustrated by way of example and not limitation inthe figures of the accompanying drawings in which like referencesindicate similar elements.

FIG. 1 illustrates an example computing system having a memorysub-system in accordance with some embodiments of the presentdisclosure.

FIG. 2 illustrates an integrated circuit memory device having acalibration circuit configured to measure signal and noisecharacteristics according to one embodiment.

FIG. 3 shows an example of measuring signal and noise characteristics toimprove memory operations according to one embodiment.

FIGS. 4-6 illustrate a technique to compute an optimized read voltagefrom count differences according to one embodiment.

FIG. 7 illustrates responses to read operations according to oneembodiment.

FIG. 8 shows a method of intelligent proactive responses to operationsof reading data from memory cells according to one embodiment.

FIG. 9 is a block diagram of an example computer system in whichembodiments of the present disclosure can operate.

DETAILED DESCRIPTION

At least some aspects of the present disclosure are directed to a memorysub-system configured to evaluate the quality of data retrievable frommemory cells and, based on the evaluation, provide intelligent proactiveresponses to operations of reading data from memory cells. Examples ofstorage devices and memory modules are described below in conjunctionwith FIG. 1 . In general, a host system can utilize a memory sub-systemthat includes one or more components, such as memory devices that storedata. The host system can provide data to be stored at the memorysub-system and can request data to be retrieved from the memorysub-system.

An integrated circuit memory cell (e.g., a flash memory cell) can beprogrammed to store data by the way of its state at a threshold voltage.For example, if the memory cell is configured/programmed in a state thatallows a substantial current to pass the memory cell at the thresholdvoltage, the memory cell is storing a bit of one; and otherwise, thememory cell is storing a bit of zero. Further, a memory cell can storemultiple bits of data by being configured/programmed differently atmultiple threshold voltages. For example, the memory cell can storemultiple bits of data by having a combination of states at the multiplethreshold voltages; and different combinations of the states of thememory cell at the threshold voltages can be interpreted to representdifferent states of bits of data that is stored in the memory cell.

However, after the states of integrated circuit memory cells areconfigured/programmed using write operations to store data in the memorycells, the optimized threshold voltage for reading the memory cells canshift due to a number of factors, such as charge loss, read disturb,cross-temperature effect (e.g., write and read at different operatingtemperatures), etc., especially when a memory cell is programmed tostore multiple bits of data.

Data can be encoded with redundant information to facilitate errordetection and recovery. When data encoded with redundant information isstored in a memory sub-system, the memory sub-system can detect errorsin raw, encoded data retrieved from the memory sub-system and/or recoverthe original, non-encoded data that is used to generate encoded data forstoring in the memory sub-system. The recovery operation can besuccessful (or have a high probability of success) when the raw, encodeddata retrieved from the memory sub-system contains less than a thresholdamount of errors, or the bit error rate in the encoded data is lowerthan a threshold. For example, error detection and data recovery can beperformed using techniques such as Error Correction Code (ECC),Low-Density Parity-Check (LDPC) code, etc.

Further, an array of independent memory devices can be configured tostore redundant information to facilitate data recovery. For example, atechnique of Redundant Array of Independent NAND (RAIN) can be used tostore redundant information, in a way similar to a redundant array ofindependent disks (RAID), to provide redundancy. Since redundant data isstored across multiple independent devices, the original data can berecovered even when one of the independent devices fails completely andlosses all of the data stored in the failed device. When the data in amemory device is determined to have corrupted, the memory sub-system canrecover the data based on redundant information stored in other memorydevices (e.g., through RAIN operations).

When the encoded data retrieved from the memory cells of the memorysub-system has too many errors for successful decoding, the memorysub-system may retry the execution of the read command with adjustedparameters for reading the memory cells. However, it is inefficient tosearch for a set of parameters through multiple read retry with multiplerounds of calibration, reading, decoding failure, and retry, until theencoded data retrieved from the memory cells can be decoded into errorfree data. For example, blind searching for the optimized read voltagesis inefficient. For example, one or more commands being injected betweenretry reads can lead to long latency for recovering data from errors.

Conventional calibration circuitry has been used to self-calibrate amemory region in applying read level signals to account for shift ofthreshold voltages of memory cells within the memory region. During thecalibration, the calibration circuitry is configured to apply differenttest signals to the memory region to count the numbers of memory cellsthat output a specified data state for the test signals. Based on thecounts, the calibration circuitry determines a read level offset valueas a response to a calibration command.

At least some aspects of the present disclosure address the above andother deficiencies by measuring signal and noise characteristics ofmemory cells, evaluating the quality of data retrievable from the memorycells based on the signal and noise characteristics, and proactivelytaking actions to facilitate recovery of error free data in a memorysub-system. The quality evaluation can be performed without using thedata retrieved from memory cells in the memory sub-system. For example,the signal and noise characteristics of memory cells can be used tocalculate a read voltage calibrated/optimized for reading the memorycells. Further, based on the signal and noise characteristics, the levelof bit error rate in data retrievable from the memory cells using thecalibrated/optimized read voltage can be classified or estimated beforethe data is retrieved from the memory cells. The estimated/classifiedlevel of bit error rate can be used to decide whether to recalibrate theread voltage, to retry read operations, to read additional data tofacilitate decoding using a sophisticated technique, to search for avoltage range for calibrating/optimizing the read voltage, and/or toperform further data recovery operations such as RAIN operations.

For example, when a read command is transmitted from a controller of amemory sub-system to a memory device enclosed in an integrated circuitpackage, the memory device can generate the signal and noisecharacteristics to evaluate the quality of the data retrievable from thememory cells in the memory device. Instead of simply providing the dataretrieved from the memory cells for the controller to analyze and/ordecode, the memory device can provide the signal and noisecharacteristics and/or the results of quality evaluation to thecontroller, suggest next actions for successful data recovery, and/orproactively initiate a next action to reduce the time to successful datarecovery. Such enriched responses by the memory device to a read commandcan enable a new paradigm of communication and cooperation between thecontroller and the memory device.

For example, in response to a command from a controller of a memorysub-system, a memory device can automatically calibrate a voltage forreading a group of memory cells based on signal and noisecharacteristics measured for memory cells. The signal and noisecharacteristics measured for memory cells can be based on a bit count ofmemory cells in the group having a predetermined status when a testvoltage is applied to read the memory cells. Different test voltagesthat are separated from one another by a predetermined voltage intervalor gap can have different bit counts. The difference between bit countsof two adjacent test voltages provides the count difference for thevoltage interval or gap between the adjacent test voltages. An optimizedread voltage can be found at a voltage where the distribution of thecount differences over voltage reaches a minimum.

When one of the count differences is smaller than its two adjacentneighbors, a minimum can be determined to be in the voltage interval orgap of the smallest count difference. An improved location of theoptimized read voltage within the gap can be computed based on a ratioof adjacent neighbors, as further discussed below in connection withFIG. 5 . Such an improved location of the optimized read voltage can beidentified as optimal.

When no count difference is between two higher adjacent neighbors, theoptimized read voltage can be identified as in a voltage interval or gapcorresponding to a count difference that is smaller than two of the nexttwo count differences. An improved location of the optimized readvoltage within the gap can be computed based on a ratio of bit counts atthe test voltages of the two ends of the gap, as further discussed belowin connection with FIG. 6 . Such an improved location of the optimizedread voltage can be identified as sub-optimal.

In some instances, the optimized read voltage of a group of memory cellsmay have changed significantly such that an initial estimation of theoptimized read voltage is too far away from the actual optimized readvoltage. Since the test voltages for calibrating/optimizing the readvoltage are configured within a narrow voltage range based on theinitial estimation of the optimized read voltage, the actual optimizedread voltage can be outside of the narrow range of test voltages used tomeasure the signal and noise characteristics and identify an estimate ofthe optimized read voltage (e.g., using the techniques of FIGS. 5 and 6). As a result, the optimized read voltage estimated using thetechniques of FIGS. 5 and 6 can be located in the incorrect test voltagerange configured to measure the signal and noise characteristics. In awider possible voltage, the narrow test voltage range may be at a wronglocation globally.

Whether the optimized read voltage estimated using the techniques ofFIGS. 5 and 6 is approximately in a correct voltage range globally canbe checked based on a statistical distribution of the threshold voltagesof the group of memory cells.

For example, a statistical distribution of the threshold voltage of amemory cell can indicate that, in a group of memory cells, the memorycells having threshold voltages higher than the actual optimized readvoltage for the group should be within a percentage range of the memorycells in the group. Thus, when reading the group of memory cells usingan optimized read voltage estimated using the techniques of FIGS. 5 and6 , the memory device can count the number of memory cells in the groupthat have threshold voltages above the estimated optimized read voltage.If the ratio between the count of such memory cells and the populationof memory cells in the group is outside of a predetermined percentagerange known for the actual optimized read voltage, the estimatedoptimized read voltage can be considered to be in a wrong voltage range.In such a situation, the memory device and/or the memory sub-system canadjust the test voltage range to search for an improved optimized readvoltage.

For example, memory cells can be programmed a page at a time; and a pageof data can be stored in a group of cells on a same wordline. The groupof cells can be identified by a particular wordline and a particularsub-block in one example; and in another example, the group can includeall of the memory cells of a particular wordline. When the memory cellsare programmed to store multiple bits per memory cell, different bits ofa memory cell can be part of different pages. For example, when a memorycell is programmed to store four bits (e.g., in a QLC mode), a first bitcan be part of a first page (e.g., referred to as lower page); a secondbit can be part of a second page (e.g., referred to as upper page); athird bit can be part of a third page (e.g., referred to as extra page);and a fourth bit can be part of a fourth page (e.g., referred to as toppage). Each page (e.g., lower page, upper page, extra page, top page)includes the set of corresponding bits in memory cells in the group.

After determining a set of optimized read voltages for reading the fourpages (e.g., lower page, upper page, extra page, top page) from a groupof memory cells in a QLC mode, the memory device can count, for each ofthe pages, the memory cells having threshold voltages that are higherthan the highest one of the optimized read voltages used to read thecorresponding page. The ratio of the cell count over the entirepopulation of memory cells in the corresponding page can be compared toa predetermined range. If the ratio is outside of the predeterminedrange, at least some of the optimized read voltages are in theincorrectly positioned test voltage ranges for their calibration.

For example, the count of memory cells in a top page having thresholdvoltages higher than the highest optimized read voltages used in readthe top page should be between 1/32 to 3/32 of the memory cells in thetop page. If the count is outside of the range for a set of readvoltages optimized/calibrated using the techniques of FIGS. 5 and 6 ,some of the test voltage ranges do not contain the actual optimized readvoltages; and the optimized/calibrated read voltages calculated based onthe test voltage ranges are determined to be in the wrong voltageranges.

Similarly, the ratio of cell count of an extra page can be compared tothe range of 3/32 to 5/32; the ratio of cell count of an upper page canbe compared to the range of 7/32 to 9/32; and the ratio of cell count ofa lower page can be compared to the range of 15/32 to 17/32.

If an optimized/calibrated read voltage is found to be based on a wrongtest voltage range, the memory device can adjust the test voltage rangeto calibrate the read voltage.

After an optimized read voltage is calculated (e.g., using techniquesillustrated in FIGS. 3-6 ), the memory device can use the optimized readvoltage to read memory cells and obtain hard bit data, and optionallyboost modulating the applied read voltage(s) to adjacent voltages tofurther read the memory cells for soft bit data.

Preferably, the operations of reading the hard bit data and reading thesoft bit data are scheduled together during the execution of the readcommand to minimize the time required to obtain the soft bit data and/orto avoid delay that can be caused by processing a separate read command,or by intervening operations on the memory cells.

Optionally, the signal and noise characteristics measured for memorycells are further used to evaluate the quality of the hard bit dataretrieved using the calibrated read voltage(s). The evaluation can beperformed at least in part concurrently with the reading of the hard bitdata. Based on the evaluated quality of the hard bit data, the memorydevice may selectively read and/or transmit the soft bit data.

The hard bit data retrieved from a group of memory cells using thecalibrated/optimized read voltage can be decoded using an errordetection and data recovery technique, such as Error Correction Code(ECC), Low-Density Parity-Check (LDPC) code, etc. When the error rate inthe hard bit data is high, the soft bit data, retrieved from the memorycell using read voltages with predetermined offsets from thecalibrated/optimized read voltage, can be used to assist the decoding ofthe hard bit data. When the soft bit data is used, the error recoverycapability is improved in decoding the hard bit data.

Optionally, a controller of a memory sub-system can initially send acommand to a memory device to read hard bit data with calibrated readvoltage; and in response to a failure in the decoding of the hard bitdata, the controller can further send a command to the memory device toread the corresponding soft bit data. Such an implementation isefficient when the likelihood of a failure in decoding the hard bit datawithout soft bit data is lower than a threshold. However, when thelikelihood is above the threshold, the overhead of sending the separatecommand becomes disadvantageous.

When the likelihood of using soft bit data is above a threshold, it isadvantageous to transmit a single command to the memory device to causethe memory device to read the soft bit data and the hard bit datatogether. Further, the memory device can use the signal and noisecharacteristics of the memory cells to predict whether the soft bit datais likely to be used by the controller. If the likelihood of using ofthe soft bit data is lower than a threshold, the memory device can skipreading the soft bit data.

For example, during the calibration operation, the memory device canmeasure the signal and noise characteristics of the memory cells and usethe measurements to calculate an optimized/calibrated read voltage forreading the memory cells. Once the optimized/calibrated read voltage isobtained, the memory device reads the memory cells to obtain the hardbit data. Subsequently, the memory device adjusts the already appliedoptimized/calibrated read voltage (e.g., through boosted modulation) toa predetermined offset (e.g., 50 mV) below the optimized/calibrated readvoltage to retrieve a set of data, and further adjusts the currentlyapplied voltage (e.g., through boosted modulation) to the predeterminedoffset above the optimized/calibrated read voltage to retrieve anotherset of data. The logic operation of XOR (exclusive or) of the two setsof data at the both sides of the offset (e.g., 50 mV) from theoptimized/calibrated read voltage provides the indication of whether thememory cells provide the same reading at the offset locations around theoptimized/calibrated read voltage. The result of the XOR operation canbe used as soft bit data for decoding the hard bit data read using theoptimized/calibrated read voltage. In some implementations, a largeroffset (e.g., 90 mV) can be used to read another set of soft bit datathat indicates whether the memory cells provide the same reading at thelocations according to the larger offset (e.g., 90 mV) around theoptimized/calibrated read voltage.

For example, in response to a read command from a controller of thememory sub-system, a memory device of the memory sub-system performs anoperation to calibrate a read voltage of memory cells. The calibrationis performed by measuring signal and noise characteristics throughreading the memory cells at a number of voltage levels that are near anestimated location of the optimized read voltage. An optimized readvoltage can be calculated based on statistical data of the resultsgenerated from reading the memory cells at the voltage levels. Forexample, the statistical data can include and/or can be based on countsmeasured by calibration circuitry at the voltage levels. Optionally,such signal and noise characteristics can be measured for sub-regions inparallel to reduce the total time for measuring the signal and noisecharacteristics. The statistical data of the results generated fromreading the memory cells at the voltage levels can be used to predictwhether the decoding of the hard bit data retrieved using the optimizedread voltage is likely to require the use of soft bit data forsuccessful decoding. Thus, the transmission of the soft bit data can beperformed selectively based on the prediction.

For example, a predictive model can be generated through machinelearning to estimate or evaluate the quality of data that can beretrieved from a set of memory cells using the calibrated/optimized readvoltage(s). The predictive model can use features calculated from themeasured signal and noise characteristics of the memory cells as inputto generate a prediction. The reading and/or transmission of the softbit data can be selectively skipped based on the prediction.

For example, as a response to a read command from the controller of thememory-subsystem, the memory device can decide that the signal and noisecharacteristics indicate a need for soft bit data in coding, and thussuggest reading soft bit data and inform the controller to extend theread time period for facilitate the reading of the soft bit data as partof the process of read the hard bit data from the memory cells. Thememory device can proactively start the process of reading the soft bitdata in connection with reading the hard bit data while transmitting thesuggestion to the controller.

For example, as a response to a read command from the controller of thememory-subsystem, the memory device can decide that the signal and noisecharacteristics indicate a need for a read retry and thus skip the datatransmission of the current read operation. The memory device canproactively start the read retry while transmitting the suggestion ofread retry to the controller such that the execution time for a readretry command from the controller can be reduced.

In some instances, the memory device can decide, based on the signal andnoise characteristics generated during the execution of a read commandto use a decoder implemented via the controller to decode the dataretrieved from the memory cells in the memory device. The decoderimplemented via the controller can use resources not available in thememory device for success decoding. For example, the decoder implementedvia the controller can use a sophisticated decoding technique to improveerror recovery capability. For example, the decoder implemented via thecontroller can use redundant information implemented via a redundantarray of independent memory devices (e.g., RAIN). In other instances,the memory device can decide that a low power decoder implemented in thememory device is sufficient to decode the data retrieved from the memorycells. Thus, the memory device can proactively start decoding whileinformation the controller that the decoding is being performed on thememory device.

For example, when the memory device determines that some of theoptimized/calibrated read voltages are in the wrong voltage ranges, thememory device can suggest a further calibration/search for the optimizedread voltages. The memory device can proactively start thecalibration/search while reporting or suggesting the furthercalibration/search.

For example, when the signal and noise characteristics indicate that thehard bit data has a low error rate, the memory sub-system can decide todecode without soft bit data. The memory device can proactively startthe decoding of the hard bit data using a decoder that has powerconsumption lower than other decoders.

For example, when the signal and noise characteristics indicate that thehard bit data has a high error rate and the optimized/calibrated readvoltages are optimal, the memory sub-system can decide to capture softbit data to decode the hard bit data using a decoder that has improvederror recovery capability. The memory device can proactively start thecapturing of the soft bit data.

For example, when the signal and noise characteristics indicate that thehard bit data has a high error rate and the optimized/calibrated readvoltages are sub-optimal, the memory sub-system can decide to adjustcalibration/optimization of the read voltages. The memory device canproactively start the next calibration/optimization.

For example, when the signal and noise characteristics indicate that theinitial optimized/calibrated read voltages are in wrong voltage ranges,the memory sub-system can decide to search for correct voltage rangesfor read voltage optimization/calibration. The memory device canproactively start the search and/or suggest voltage ranges.

For example, when the signal and noise characteristics indicate that itis unlikely to recover data from a group of memory cells in the memorydevice, the memory sub-system can decide to use RAIN operations torecover the data from redundant information stored in other memorydevices.

FIG. 1 illustrates an example computing system 100 that includes amemory sub-system 110 in accordance with some embodiments of the presentdisclosure. The memory sub-system 110 can include media, such as one ormore volatile memory devices (e.g., memory device 140), one or morenon-volatile memory devices (e.g., memory device 130), or a combinationof such.

A memory sub-system 110 can be a storage device, a memory module, or ahybrid of a storage device and memory module. Examples of a storagedevice include a solid-state drive (SSD), a flash drive, a universalserial bus (USB) flash drive, an embedded Multi-Media Controller (eMMC)drive, a Universal Flash Storage (UFS) drive, a secure digital (SD)card, and a hard disk drive (HDD). Examples of memory modules include adual in-line memory module (DIMM), a small outline DIMM (SO-DIMM), andvarious types of non-volatile dual in-line memory module (NVDIMM).

The computing system 100 can be a computing device such as a desktopcomputer, laptop computer, network server, mobile device, a vehicle(e.g., airplane, drone, train, automobile, or other conveyance),Internet of Things (IoT) enabled device, embedded computer (e.g., oneincluded in a vehicle, industrial equipment, or a networked commercialdevice), or such computing device that includes memory and a processingdevice.

The computing system 100 can include a host system 120 that is coupledto one or more memory sub-systems 110. FIG. 1 illustrates one example ofa host system 120 coupled to one memory sub-system 110. As used herein,“coupled to” or “coupled with” generally refers to a connection betweencomponents, which can be an indirect communicative connection or directcommunicative connection (e.g., without intervening components), whetherwired or wireless, including connections such as electrical, optical,magnetic, etc.

The host system 120 can include a processor chipset (e.g., processingdevice 118) and a software stack executed by the processor chipset. Theprocessor chipset can include one or more cores, one or more caches, amemory controller (e.g., controller 116) (e.g., NVDIMM controller), anda storage protocol controller (e.g., PCIe controller, SATA controller).The host system 120 uses the memory sub-system 110, for example, towrite data to the memory sub-system 110 and read data from the memorysub-system 110.

The host system 120 can be coupled to the memory sub-system 110 via aphysical host interface. Examples of a physical host interface include,but are not limited to, a serial advanced technology attachment (SATA)interface, a peripheral component interconnect express (PCIe) interface,universal serial bus (USB) interface, Fibre Channel, Serial AttachedSCSI (SAS), a double data rate (DDR) memory bus, Small Computer SystemInterface (SCSI), a dual in-line memory module (DIMM) interface (e.g.,DIMM socket interface that supports Double Data Rate (DDR)), Open NANDFlash Interface (ONFI), Double Data Rate (DDR), Low Power Double DataRate (LPDDR), or any other interface. The physical host interface can beused to transmit data between the host system 120 and the memorysub-system 110. The host system 120 can further utilize an NVM Express(NVMe) interface to access components (e.g., memory devices 130) whenthe memory sub-system 110 is coupled with the host system 120 by thePCIe interface. The physical host interface can provide an interface forpassing control, address, data, and other signals between the memorysub-system 110 and the host system 120. FIG. 1 illustrates a memorysub-system 110 as an example. In general, the host system 120 can accessmultiple memory sub-systems via a same communication connection,multiple separate communication connections, and/or a combination ofcommunication connections.

The processing device 118 of the host system 120 can be, for example, amicroprocessor, a central processing unit (CPU), a processing core of aprocessor, an execution unit, etc. In some instances, the controller 116can be referred to as a memory controller, a memory management unit,and/or an initiator. In one example, the controller 116 controls thecommunications over a bus coupled between the host system 120 and thememory sub-system 110. In general, the controller 116 can send commandsor requests to the memory sub-system 110 for desired access to memorydevices 130,140. The controller 116 can further include interfacecircuitry to communicate with the memory sub-system 110. The interfacecircuitry can convert responses received from the memory sub-system 110into information for the host system 120.

The controller 116 of the host system 120 can communicate with thecontroller 115 of the memory sub-system 110 to perform operations suchas reading data, writing data, or erasing data at the memory devices130,140 and other such operations. In some instances, the controller 116is integrated within the same package of the processing device 118. Inother instances, the controller 116 is separate from the package of theprocessing device 118. The controller 116 and/or the processing device118 can include hardware such as one or more integrated circuits (ICs)and/or discrete components, a buffer memory, a cache memory, or acombination thereof. The controller 116 and/or the processing device 118can be a microcontroller, special purpose logic circuitry (e.g., a fieldprogrammable gate array (FPGA), an application specific integratedcircuit (ASIC), etc.), or another suitable processor.

The memory devices 130, 140 can include any combination of the differenttypes of non-volatile memory components and/or volatile memorycomponents. The volatile memory devices (e.g., memory device 140) canbe, but are not limited to, random access memory (RAM), such as dynamicrandom access memory (DRAM) and synchronous dynamic random access memory(SDRAM).

Some examples of non-volatile memory components include a negative-and(or, NOT AND) (NAND) type flash memory and write-in-place memory, suchas three-dimensional cross-point (“3D cross-point”) memory. Across-point array of non-volatile memory can perform bit storage basedon a change of bulk resistance, in conjunction with a stackablecross-gridded data access array. Additionally, in contrast to manyflash-based memories, cross-point non-volatile memory can perform awrite in-place operation, where a non-volatile memory cell can beprogrammed without the non-volatile memory cell being previously erased.NAND type flash memory includes, for example, two-dimensional NAND (2DNAND) and three-dimensional NAND (3D NAND).

Each of the memory devices 130 can include one or more arrays of memorycells. One type of memory cell, for example, single level cells (SLC)can store one bit per cell. Other types of memory cells, such asmulti-level cells (MLCs), triple level cells (TLCs), quad-level cells(QLCs), and penta-level cells (PLC) can store multiple bits per cell. Insome embodiments, each of the memory devices 130 can include one or morearrays of memory cells such as SLCs, MLCs, TLCs, QLCs, or anycombination of such. In some embodiments, a particular memory device caninclude an SLC portion, and an MLC portion, a TLC portion, or a QLCportion of memory cells. The memory cells of the memory devices 130 canbe grouped as pages that can refer to a logical unit of the memorydevice used to store data. With some types of memory (e.g., NAND), pagescan be grouped to form blocks.

Although non-volatile memory devices such as 3D cross-point type andNAND type memory (e.g., 2D NAND, 3D NAND) are described, the memorydevice 130 can be based on any other type of non-volatile memory, suchas read-only memory (ROM), phase change memory (PCM), self-selectingmemory, other chalcogenide based memories, ferroelectric transistorrandom-access memory (FeTRAM), ferroelectric random access memory(FeRAM), magneto random access memory (MRAM), Spin Transfer Torque(STT)-MRAM, conductive bridging RAM (CBRAM), resistive random accessmemory (RRAM), oxide based RRAM (OxRAM), negative-or (NOR) flash memory,and electrically erasable programmable read-only memory (EEPROM).

A memory sub-system controller 115 (or controller 115 for simplicity)can communicate with the memory devices 130 to perform operations suchas reading data, writing data, or erasing data at the memory devices 130and other such operations (e.g., in response to commands scheduled on acommand bus by controller 116). The controller 115 can include hardwaresuch as one or more integrated circuits (ICs) and/or discretecomponents, a buffer memory, or a combination thereof. The hardware caninclude digital circuitry with dedicated (i.e., hard-coded) logic toperform the operations described herein. The controller 115 can be amicrocontroller, special purpose logic circuitry (e.g., a fieldprogrammable gate array (FPGA), an application specific integratedcircuit (ASIC), etc.), or another suitable processor.

The controller 115 can include a processing device 117 (processor)configured to execute instructions stored in a local memory 119. In theillustrated example, the local memory 119 of the controller 115 includesan embedded memory configured to store instructions for performingvarious processes, operations, logic flows, and routines that controloperation of the memory sub-system 110, including handlingcommunications between the memory sub-system 110 and the host system120.

In some embodiments, the local memory 119 can include memory registersstoring memory pointers, fetched data, etc. The local memory 119 canalso include read-only memory (ROM) for storing micro-code. While theexample memory sub-system 110 in FIG. 1 has been illustrated asincluding the controller 115, in another embodiment of the presentdisclosure, a memory sub-system 110 does not include a controller 115,and can instead rely upon external control (e.g., provided by anexternal host, or by a processor or controller separate from the memorysub-system).

In general, the controller 115 can receive commands or operations fromthe host system 120 and can convert the commands or operations intoinstructions or appropriate commands to achieve the desired access tothe memory devices 130. The controller 115 can be responsible for otheroperations such as wear leveling operations, garbage collectionoperations, error detection and error-correcting code (ECC) operations,encryption operations, caching operations, and address translationsbetween a logical address (e.g., logical block address (LBA), namespace)and a physical address (e.g., physical block address) that areassociated with the memory devices 130. The controller 115 can furtherinclude host interface circuitry to communicate with the host system 120via the physical host interface. The host interface circuitry canconvert the commands received from the host system into commandinstructions to access the memory devices 130 as well as convertresponses associated with the memory devices 130 into information forthe host system 120.

The memory sub-system 110 can also include additional circuitry orcomponents that are not illustrated. In some embodiments, the memorysub-system 110 can include a cache or buffer (e.g., DRAM) and addresscircuitry (e.g., a row decoder and a column decoder) that can receive anaddress from the controller 115 and decode the address to access thememory devices 130.

In some embodiments, the memory devices 130 include local mediacontrollers 150 that operate in conjunction with the memory sub-systemcontroller 115 to execute operations on one or more memory cells of thememory devices 130. An external controller (e.g., memory sub-systemcontroller 115) can externally manage the memory device 130 (e.g.,perform media management operations on the memory device 130). In someembodiments, a memory device 130 is a managed memory device, which is araw memory device combined with a local controller (e.g., localcontroller 150) for media management within the same memory devicepackage. An example of a managed memory device is a managed NAND (MNAND)device.

The controller 115 and/or a memory device 130 can include a read manager113 configured to determine a proactive response for reading data from agroup of memory cells based on signal and noise characteristics of thegroup of memory cells. In some embodiments, the controller 115 in thememory sub-system 110 includes at least a portion of the read manager113. In other embodiments, or in combination, the controller 116 and/orthe processing device 118 in the host system 120 includes at least aportion of the read manager 113. For example, the controller 115, thecontroller 116, and/or the processing device 118 can include logiccircuitry implementing the read manager 113. For example, the controller115, or the processing device 118 (processor) of the host system 120,can be configured to execute instructions stored in memory forperforming the operations of the read manager 113 described herein. Insome embodiments, the read manager 113 is implemented in an integratedcircuit chip disposed in the memory sub-system 110. In otherembodiments, the read manager 113 can be part of firmware of the memorysub-system 110, an operating system of the host system 120, a devicedriver, or an application, or any combination therein.

For example, the read manager 113 implemented in the controller 115 cantransmit a read command or a calibration command to the memory device130. In response to such a command, the read manager 113 implemented inthe memory device 130 is configured to measure signal and noisecharacteristics of a group of memory cells by reading the group ofmemory cells at a plurality of test voltages configured near anestimated location of the optimized read voltage for the group of memorycells. The test voltages can be configured to be equally spaced by asame amount of voltage gap. From a result of reading the group of memorycells at a test voltage, a bit count of memory cells in the group aredetermined to be storing or reporting a predetermined bit (e.g., 0 or 1corresponding to memory cells being conductive or non-conductive at thetest voltage) when the group is read at the test voltage. A countdifference can be computed from the bit counts of each pair of adjacenttest voltages. The read manager 113 compares the count difference toidentify a voltage interval that contains an optimized read voltage andthen estimates a location in the voltage interval for the optimized readvoltage based on comparing the bit counts or count differences that areclosest to the voltage interval. The estimated location can be used asthe optimized read voltage to read hard bit data; and voltages havingpredetermined offsets from the optimized read voltage can be used toread soft bit data.

The quality of the hard bit data and/or the soft bit data can beevaluated based on the signal and noise characteristics of the group ofmemory cells that are used to determine the estimated location of theoptimized read voltage. Based on the evaluated quality, the memorysub-system can suggest proactive actions such as decode hard bit datawithout soft bit data, read soft bit data, refinecalibration/optimization of the read voltages, search for test voltageranges to perform calibration/optimization of the read voltages, etc.

FIG. 2 illustrates an integrated circuit memory device 130 having acalibration circuit 145 configured to measure signal and noisecharacteristics according to one embodiment. For example, the memorydevices 130 in the memory sub-system 110 of FIG. 1 can be implementedusing the integrated circuit memory device 130 of FIG. 2 .

The integrated circuit memory device 130 can be enclosed in a singleintegrated circuit package. The integrated circuit memory device 130includes multiple groups 131, . . . , 133 of memory cells that can beformed in one or more integrated circuit dies. A typical memory cell ina group 131, . . . , 133 can be programmed to store one or more bits ofdata.

Some of the memory cells in the integrated circuit memory device 130 canbe configured to be operated together for a particular type ofoperations. For example, memory cells on an integrated circuit die canbe organized in planes, blocks, and pages. A plane contains multipleblocks; a block contains multiple pages; and a page can have multiplestrings of memory cells. For example, an integrated circuit die can bethe smallest unit that can independently execute commands or reportstatus; identical, concurrent operations can be executed in parallel onmultiple planes in an integrated circuit die; a block can be thesmallest unit to perform an erase operation; and a page can be thesmallest unit to perform a data program operation (to write data intomemory cells). Each string has its memory cells connected to a commonbitline; and the control gates of the memory cells at the same positionsin the strings in a block or page are connected to a common wordline.Control signals can be applied to wordlines and bitlines to address theindividual memory cells.

The integrated circuit memory device 130 has a communication interface147 to receive a command having an address 135 from the controller 115of a memory sub-system 110, retrieve both hard bit data 177 and soft bitdata 173 from the memory address 135, and provide at least the hard bitdata 177 as a response to the command. An address decoder 141 of theintegrated circuit memory device 130 converts the address 135 intocontrol signals to select a group of memory cells in the integratedcircuit memory device 130; and a read/write circuit 143 of theintegrated circuit memory device 130 performs operations to determinethe hard bit data 177 and the soft bit data 173 of memory cells at theaddress 135.

The integrated circuit memory device 130 has a calibration circuit 145configured to determine measurements of signal and noise characteristics139 of memory cells in a group (e.g., 131, . . . , or 133). For example,the statistics of memory cells in a group or region that has aparticular state at one or more test voltages can be measured todetermine the signal and noise characteristics 139. Optionally, thesignal and noise characteristics 139 can be provided by the memorydevice 130 to the controller 115 of a memory sub-system 110 via thecommunication interface 147.

In at least some embodiments, the calibration circuit 145 determines theoptimized read voltage(s) of the group of memory cells based on thesignal and noise characteristics 139. In some embodiments, the signaland noise characteristics 139 are further used in the calibrationcircuit 145 to determine whether the error rate in the hard bit data 177is sufficiently high such that it is preferred to decode the hard bitdata 177 in combination with the soft bit data 173 using a sophisticateddecoder. When the use of the soft bit data 173 is predicted, based onthe prediction/classification of the error rate in the hard bit data177, the read manager 113 can transmit both the soft bit data 173 andthe hard bit data 177 to the controller 115 of the memory sub-system110.

For example, the calibration circuit 145 can measure the signal andnoise characteristics 139 by reading different responses from the memorycells in a group (e.g., 131, . . . , 133) by varying operatingparameters used to read the memory cells, such as the voltage(s) appliedduring an operation to read data from memory cells.

For example, the calibration circuit 145 can measure the signal andnoise characteristics 139 on the fly when executing a command to readthe hard bit data 177 and the soft bit data 173 from the address 135.Since the signal and noise characteristics 139 is measured as part ofthe operation to read the hard bit data 177 from the address 135, thesignal and noise characteristics 139 can be used in the read manager 113with reduced or no penalty on the latency in the execution of thecommand to read the hard bit data 177 from the address 135.

The read manager 113 of the memory device 130 is configured to use thesignal and noise characteristics 139 to determine the voltages used toread memory cells identified by the address 135 for both hard bit dataand soft bit data and to determine whether to transmit the soft bit datato the memory sub-system controller 115.

For example, the read manager 113 can use a predictive model, trainedvia machine learning, to predict the likelihood of the hard bit data 177retrieved from a group of memory cells (e.g., 131 or 133) failing a testof data integrity. The prediction can be made based on the signal andnoise characteristics 139. Before the test is made usingerror-correcting code (ECC) and/or low-density parity-check (LDPC) code,or even before the hard bit data 177 is transferred to a decoder, theread manager 113 uses the signal and noise characteristics 139 topredict the result of the test. Based on the predicted result of thetest, the read manager 113 determines whether to transmit the soft bitdata to the memory sub-system controller 115 in a response to thecommand.

For example, if the hard bit data 177 is predicted to decode using alow-power decoder that uses hard bit data 177 without using the soft bitdata 173, the read manager 113 can skip the transmission of the soft bitdata 173 to the memory sub-system controller 115; and the read manager113 provides the hard bit data 177, read from the memory cells usingoptimized read voltages calculated from the signal and noisecharacteristics 139, for decoding by the low-power decoder. For example,the low-power decoder can be implemented in the memory sub-systemcontroller 115. Alternatively, the low-power decoder can be implementedin the memory device 130; and the read manager 113 can provide theresult of the lower-power decoder to the memory sub-system controller115 as the response to the received command.

For example, if the hard bit data 177 is predicted to fail in decodingin the low-power decoder but can be decoded using a high-power decoderthat uses both hard bit data and soft bit data, the read manager 113 candecide to provide both the hard bit data 177 and the soft bit data 173for decoding by the high-power decoder. For example, the high-powerdecoder can be implemented in the controller 115. Alternatively, thehigh-power decoder can be implemented in the memory device 130.

Optionally, if the hard bit data 173 is predicted to fail in decoding indecoders available in the memory sub-system 110, the read manager 113can decide to skip transmitting the hard bit data 173 to the memorysub-system controller 115, initiate a read retry immediately, such thatwhen the memory sub-system controller 115 requests a read retry, atleast a portion of the read retry operations is performed to reduce thetime for responding to the request from the memory sub-system controller115 for a read retry. For example, during the read retry, the readmanager 113 instructs the calibration circuit 145 to perform a modifiedcalibration to obtain a new set of signal and noise characteristics 139,which can be further used to determine improved read voltages.

The data from the memory cells identified by the address (135) caninclude hard bit data 177 and soft bit data 173. The hard bit data 177is retrieved using optimized read voltages. The hard bit data 177identifies the states of the memory cells that are programmed to storedata and subsequently detected in view of changes caused by factors,such as charge loss, read disturb, cross-temperature effect (e.g., writeand read at different operating temperatures), etc. The soft bit data173 is obtained by reading the memory cells using read voltages centeredat each optimized read voltage with a predetermined offset from thecenter, optimized read voltage. The XOR of the read results at the readvoltages having the offset indicates whether the memory cells providedifferent read results at the read voltages having the offset. The softbit data 173 can include the XOR results. In some instances, one set ofXOR results is obtained based on a smaller offset; and another set ofXOR results is obtained based on a larger offset. In general, multiplesets of XOR results can be obtained for multiple offsets, where eachrespective offset is used to determine a lower read voltage and a higherread voltage such that both the lower and higher read voltages have thesame respective offset from an optimized read voltage to determine theXOR results.

FIG. 3 shows an example of measuring signal and noise characteristics139 to improve memory operations according to one embodiment.

In FIG. 3 , the calibration circuit 145 applies different read voltagesV_(A), V_(B), V_(C), V_(D), and V_(E) to read the states of memory cellsin a group (e.g., 131, . . . , or 133). In general, more or less readvoltages can be used to generate the signal and noise characteristics139.

As a result of the different voltages applied during the read operation,a same memory cell in the group (e.g., 131, . . . , or 133) may showdifferent states. Thus, the counts C_(A), C_(B), C_(C), C_(D), and C_(E)of memory cells having a predetermined state at different read voltagesV_(A), V_(B), V_(C), V_(D), and V_(E) can be different in general. Thepredetermined state can be a state of having substantial current passingthrough the memory cells, or a state of having no substantial currentpassing through the memory cells. The counts C_(A), C_(B), C_(C), C_(D),and C_(E) can be referred to as bit counts.

The calibration circuit 145 can measure the bit counts by applying theread voltages V_(A), V_(B), V_(C), V_(D), and V_(E) one at a time on thegroup (e.g., 131, . . . , or 133) of memory cells.

Alternatively, the group (e.g., 131, . . . , or 133) of memory cells canbe configured as multiple subgroups; and the calibration circuit 145 canmeasure the bit counts of the subgroups in parallel by applying the readvoltages V_(A), V_(B), V_(C), V_(D), and V_(E). The bit counts of thesubgroups are considered as representative of the bit counts in theentire group (e.g., 131, . . . , or 133). Thus, the time duration ofobtaining the counts C_(A), C_(B), C_(C), C_(D), and C_(E) can bereduced.

In some embodiments, the bit counts C_(A), C_(B), C_(C), C_(D), andC_(E) are measured during the execution of a command to read the datafrom the address 135 that is mapped to one or more memory cells in thegroup (e.g., 131, . . . , or 133). Thus, the controller 115 does notneed to send a separate command to request for the signal and noisecharacteristics 139 that is based on the bit counts C_(A), C_(B), C_(C),C_(D), and C_(E).

The differences between the bit counts of the adjacent voltages areindicative of the errors in reading the states of the memory cells inthe group (e.g., 133, . . . , or 133).

For example, the count difference D_(A) is calculated from C_(A)−C_(B),which is an indication of read threshold error introduced by changingthe read voltage from V_(A) to V_(B).

Similarly, D_(B)=C_(B)−C_(C); D_(C)=C_(C)−C_(D); and D_(D)=C_(D)−C_(E).

The curve 157, obtained based on the count differences D_(A), D_(B),D_(C), and D_(D), represents the prediction of read threshold error E asa function of the read voltage. From the curve 157 (and/or the countdifferences), the optimized read voltage V_(O) can be calculated as thepoint 153 that provides the lowest read threshold error D_(MIN) on thecurve 157.

In one embodiment, the calibration circuit 145 computes the optimizedread voltage V_(O) and causes the read/write circuit 143 to read thedata from the address 135 using the optimized read voltage V_(O).

Alternatively, the calibration circuit 145 can provide, via thecommunication interface 147 to the controller 115 of the memorysub-system 110, the count differences D_(A), D_(B), D_(C), and D_(D)and/or the optimized read voltage V_(O) calculated by the calibrationcircuit 145.

FIG. 3 illustrates an example of generating a set of statistical data(e.g., bit counts and/or count differences) for reading at an optimizedread voltage V_(O). In general, a group of memory cells can beconfigured to store more than one bit in a memory cell; and multipleread voltages are used to read the data stored in the memory cells. Aset of statistical data can be similarly measured for each of the readvoltages to identify the corresponding optimized read voltage, where thetest voltages in each set of statistical data are configured in thevicinity of the expected location of the corresponding optimized readvoltage. Thus, the signal and noise characteristics 139 measured for amemory cell group (e.g., 131 or 133) can include multiple sets ofstatistical data measured for the multiple threshold voltagesrespectively.

For example, the controller 115 can instruct the memory device 130 toperform a read operation by providing an address 135 and at least oneread control parameter. For example, the read control parameter can be asuggested read voltage.

The memory device 130 can perform the read operation by determining thestates of memory cells at the address 135 at a read voltage and providethe data according to the determined states.

During the read operation, the calibration circuit 145 of the memorydevice 130 generates the signal and noise characteristics 139. The dataand the signal and noise characteristics 139 are provided from thememory device 130 to the controller 115 as a response. Alternatively,the processing of the signal and noise characteristics 139 can beperformed at least in part using logic circuitry configured in thememory device 130. For example, the processing of the signal and noisecharacteristics 139 can be implemented partially or entirely using theprocessing logic configured in the memory device 130. For example, theprocessing logic can be implemented using Complementarymetal-oxide-semiconductor (CMOS) circuitry formed under the array ofmemory cells on an integrated circuit die of the memory device 130. Forexample, the processing logic can be formed, within the integratedcircuit package of the memory device 130, on a separate integratedcircuit die that is connected to the integrated circuit die having thememory cells using Through-Silicon Vias (TSVs) and/or other connectiontechniques.

The signal and noise characteristics 139 can be determined based atleast in part on the read control parameter. For example, when the readcontrol parameter is a suggested read voltage for reading the memorycells at the address 135, the calibration circuit 145 can compute theread voltages V_(A), V_(B), V_(C), V_(D), and V_(E) that are in thevicinity of the suggested read voltage.

The signal and noise characteristics 139 can include the bit countsC_(A), C_(B), C_(C), C_(D), and C_(E). Alternatively, or in combination,the signal and noise characteristics 139 can include the countdifferences D_(A), D_(B), D_(C), and D_(D).

Optionally, the calibration circuit 145 uses one method to compute anoptimized read voltage V_(O) from the count differences D_(A), D_(B),D_(C), and D_(D); and the controller 115 uses another different methodto compute the optimized read voltage V_(O) from the signal and noisecharacteristics 139 and optionally other data that is not available tothe calibration circuit 145.

When the calibration circuit 145 can compute the optimized read voltageV_(O) from the count differences D_(A), D_(B), D_(C), and D_(D)generated during the read operation, the signal and noisecharacteristics can optionally include the optimized read voltage V_(O).Further, the memory device 130 can use the optimized read voltage V_(O)in determining the hard bit data in the data from the memory cells atthe address 135. The soft bit data in the data can be obtained byreading the memory cells with read voltages that are a predeterminedoffset away from the optimized read voltage V_(O). Alternatively, thememory device 130 uses the controller-specified read voltage provided inthe read control parameter in reading the data.

The controller 115 can be configured with more processing power than thecalibration circuit 145 of the integrated circuit memory device 130.Further, the controller 115 can have other signal and noisecharacteristics applicable to the memory cells in the group (e.g., 133,. . . , or 133). Thus, in general, the controller 115 can compute a moreaccurate estimation of the optimized read voltage V_(O) (e.g., for asubsequent read operation, or for a retry of the read operation).

In general, it is not necessary for the calibration circuit 145 toprovide the signal and noise characteristics 139 in the form of adistribution of bit counts over a set of read voltages, or in the formof a distribution of count differences over a set of read voltages. Forexample, the calibration circuit 145 can provide the optimized readvoltage V_(O) calculated by the calibration circuit 145, as signal andnoise characteristics 139.

The calibration circuit 145 can be configured to generate the signal andnoise characteristics 139 (e.g., the bit counts, or bit countdifferences) as a byproduct of a read operation. The generation of thesignal and noise characteristics 139 can be implemented in theintegrated circuit memory device 130 with little or no impact on thelatency of the read operation in comparison with a typical read withoutthe generation of the signal and noise characteristics 139. Thus, thecalibration circuit 145 can determine signal and noise characteristics139 efficiently as a byproduct of performing a read operation accordingto a command from the controller 115 of the memory sub-system 110.

In general, the calculation of the optimized read voltage V_(O) can beperformed within the memory device 130, or by a controller 115 of thememory sub-system 110 that receives the signal and noise characteristics139 as part of enriched status response from the memory device 130.

The hard bit data 177 can be obtained by applying the optimized readvoltage V_(O) on the group of memory cells and determining the state ofthe memory cells while the memory cells are subjected to the optimizedread voltages V_(O).

The soft bit data 173 can be obtained by applying the read voltages 181and 182 that are offset from the optimized read voltage V_(O) with apredetermined amount. For example, the read voltage 181 is at the offset183 of the predetermined amount lower from the optimized read voltageV_(O); and the read voltage 182 is at the offset 184 of the samepredetermined amount higher from the optimized read voltage V_(O). Amemory cell subjected to the read voltage 181 can have a state that isdifferent from the memory cell subjected to the read voltage 182. Thesoft bit data 173 can include or indicate the XOR result of the dataread from the memory cell using the read voltages 181 and 182. The XORresult shows whether the memory cell subjected to the read voltage 181has the same state as being to the read voltage 182.

FIGS. 4-6 illustrate a technique to compute an optimized read voltagefrom count differences according to one embodiment. The technique ofFIGS. 4-6 simplifies the computation for calculating the optimized readvoltage V_(O) such that the computation can be implemented using reducedcomputing power and/or circuitry.

The computation illustrated in FIGS. 4-6 can be performed based on thebit counts and count differences illustrated in FIG. 3 for test voltagesV_(A), V_(B), V_(C), V_(D), and V_(E).

In FIG. 4 , an operation 201 is performed to compare the two centercount differences D_(B) and D_(C).

If D_(B) is greater than D_(C), it can be assumed that a minimal can befound on the higher half of the test voltage region between V_(C) toV_(E). Thus, operation 203 is performed to compare the lower one D_(C)of the two center bit count differences with its other neighbor D_(D).

If D_(C) is no greater than its other neighbor D_(D), D_(C) is nogreater than its neighbors D_(B) and D_(D). Thus, it can be inferredthat a minimal can be found between the test voltages V_(C) and V_(D).Based on a ratio between the differences of D_(C) from its neighborsD_(B) and D_(D), an estimate of the location of the optimized readvoltage V_(O) can be determined using a technique similar to thatillustrated in FIG. 5 .

If D_(C) is greater than its other neighbor D_(D), it can be assumedthat a minimal can be in the highest test voltage interval between V_(D)and V_(E). Thus, an estimate of the location of the optimized readvoltage V_(O) can be determined using a technique similar to thatillustrated in FIG. 6 , based on a ratio of count differences D_(D) andD_(C) that are closest to the test voltages V_(D) and V_(E).

Similarly, if D_(B) is no greater than D_(C), it can be assumed that aminimal can be found on the lower half of the test voltage regionbetween V_(A) to V_(C). Thus, operation 205 is performed to compare thelower one D_(B) of the two center bit count differences with its otherneighbor D_(A).

If D_(B) is less than its other neighbor D_(A), D_(B) is no greater thanits neighbors D_(A) and D_(C). Thus, it can be inferred that a minimalcan be found between the test voltages V_(B) and V_(C). Based on a ratiobetween the differences of D_(B) from its neighbors D_(A) and D_(C), anestimate of the location of the optimized read voltage V_(O) can bedetermined using a technique illustrated in FIG. 5 .

If D_(B) is no less than its other neighbor D_(A), it can be assumedthat a minimal can be in the lowest test voltage interval between V_(A)and V_(B). Thus, an estimate of the location of the optimized readvoltage V_(O) can be determined using a technique illustrated in FIG. 6, based on a ratio of the count differences D_(A) and D_(B) that areclosest to the test voltages V_(A) and V_(B).

FIG. 5 illustrates a technique to estimate the location of the optimizedread voltage V_(O) when a center count difference D_(B) is no greaterthan its neighbors D_(A) and D_(C).

Since the count difference D_(B) is the difference of bit counts C_(B)and C_(C) at test voltages V_(B) and V_(C), the location of theoptimized read voltage V_(O) is estimated to be within the voltageinterval or gap between V_(B) and V_(C).

When the increases from the center count difference D_(B) to itsneighbors D_(A) and D_(C) are substantially equal to each other, theoptimized read voltage V_(O) is estimated at the midpoint between V_(B)and V_(C).

The ratio between the increases from the center count difference D_(B)to its neighbors D_(A) and D_(C) can be mapped in a logarithmic scale toa line scale of division between the test voltages V_(B) and V_(C).

For example, the ratio (D_(A)−D_(B))/(D_(C)−D_(B)) of 1 is mapped to alocation of the optimized read voltage at the midpoint between the testvoltages V_(B) and V_(C).

The ratio (D_(A)−D_(B))/(D_(C)−D_(B)) of ½ is mapped to a location ofthe optimized read voltage at the midpoint between the test voltagesV_(B) and V_(C) with an offset of a fixed increment towards V_(B). Forexample, the increment can be one tenth of the voltage gap between V_(B)and V_(C).

Similarly, the ratio (D_(A)−D_(B))/(D_(C)−D_(B)) of ¼, ⅛, or 1/16 ismapped to a location of the optimized read voltage at the midpointbetween the test voltages V_(B) and V_(C) with an offset of two, three,or four increments towards V_(B). A ratio (D_(A)−D_(B))/(D_(C)−D_(B))smaller than 1/16 can be mapped to a location of the optimized readvoltage at V_(B).

Similarly, the ratio (D_(C)−D_(B))/(D_(A)−D_(B)) of ½, ¼, ⅛, or 1/16 ismapped to a location of the optimized read voltage at the midpointbetween the test voltages V_(B) and V_(C) with an offset of one, two,three, or four increments towards V_(C). A ratio(D_(C)−D_(B))/(D_(A)−D_(B)) smaller than 1/16 can be mapped to alocation of the optimized read voltage at V_(C).

The technique of FIG. 5 can be implemented via setting a coarseestimation of the optimized read voltage at V_(B) (or V_(C)) andadjusting the coarse estimation through applying the increment accordingto comparison of the increase (D_(A)−D_(B)) of the count differenceD_(B) to the count difference D_(A) with fractions or multiples of theincrease (D_(C)−D_(B)) of the count difference D_(B) to the countdifference D_(C). The fractions or multiples of the increase(D_(C)−D_(B)) in a logarithmic scale can be computed through iterativedivision or multiplication by two, which can be implemented efficientlythrough bit-wise left shift or right shift operations.

For example, the initial estimate of the optimized voltage V_(O) can beset at the test voltage V_(B). The increase (D_(A)—D_(B)) can becompared with (D_(C)−D_(B))/16, which can be computed through shiftingthe bits of (D_(C)−D_(B)). If (D_(A)−D_(B)) is greater than(D_(C)−D_(B))/16, the increment of one tenth of the gap between V_(B)and V_(C) can be added to the estimate of the optimized voltage V_(O).Subsequently, (D_(A)−D_(B)) is compared to (D_(C)−D_(B))/8, which can becalculated by shifting the bits of (D_(C)−D_(B))/16. If (D_(A)−D_(B)) isgreater than (D_(C)−D_(B))/8, the same increment of one tenth of the gapbetween V_(B) and V_(C) is further added to the estimation of theoptimized voltage V_(O). Similarly, (D_(A)−D_(B)) is compared to(D_(C)−D_(B))/4, (D_(C)−D_(B))/2, (D_(C)−D_(B)), (D_(C)−D_(B))*2,(D_(C)−D_(B))*4, (D_(C)−D_(B))*8, and (D_(C)−D_(B))*16 one afteranother. If (D_(A)−D_(B)) is greater than any of these scaled versionsof (D_(C)−D_(B)) in a comparison, the same increment is added to theestimate. After the series of comparisons, the resulting estimate can beused as the optimized voltage V_(O).

FIG. 6 illustrates a technique to estimate the location of the optimizedread voltage V_(O) when a side count difference D_(A) is smaller thanits next two count differences D_(B) and D_(C), but one of its neighborshas not been measured (e.g., a count difference between the test voltageV_(A) and a further test voltage that is lower than V_(A)).

Since the count difference D_(A) is the lowest among count differencesD_(A), D_(B) and D_(C), the optimized voltage V_(O) is estimated to bein the test voltage interval or gap corresponding to the countdifference D_(A). Since the count difference D_(A) is the difference ofbit counts C_(A) and C_(B) at test voltages V_(A) and V_(B), thelocation of the optimized read voltage V_(O) is estimated to be withinthe voltage interval or gap between V_(A) and V_(B).

In FIG. 6 , the location of the optimized read voltage V_(O) within thevoltage interval or gap between V_(A) and V_(B) is based on a ratio ofthe count differences D_(A) and D_(B). The ratio D_(A)/D_(B) in alogarithmic scale is mapped to the linear distribution of the optimizedread voltage V_(O) between V_(A) and V_(B).

For example, the voltage interval or gap between V_(A) and V_(B) can bedivided into five equal increments. The initial estimate of theoptimized voltage V_(O) can be set at the test voltage V_(B). The countdifference D_(A) can be compared to scaled versions of the countdifference D_(B) sequentially, such as D_(B), D_(B)/2, and D_(B)/4. Ifthe count difference D_(A) is smaller than any of the scaled versions ofthe count difference D_(B) in a comparison, the estimate is reduced bythe increment for moving towards the test voltage V_(A).

In one embodiment, a method is implemented to calculate an optimizedread voltage for reading a group of memory cells. The method can beperformed by processing logic that can include hardware (e.g.,processing device, circuitry, dedicated logic, programmable logic,microcode, hardware of a device, integrated circuit, etc.),software/firmware (e.g., instructions run or executed on a processingdevice), or a combination thereof. In some embodiments, the method isperformed at least in part by the controller 115 of FIG. 1 , orprocessing logic in the memory device 130 of FIG. 2 . Although shown ina particular sequence or order, unless otherwise specified, the order ofthe processes can be modified. Thus, the illustrated embodiments shouldbe understood only as examples, and the illustrated processes can beperformed in a different order, and some processes can be performed inparallel. Additionally, one or more processes can be omitted in variousembodiments. Thus, not all processes are required in every embodiment.Other process flows are possible. For example, the method can beimplemented in a computing system of FIG. 1 with a memory device of FIG.2 and signal noise characteristics illustrated in FIG. 3 with some ofthe operations illustrated in FIGS. 4-6 .

In the method, a memory device 130 reads a group of memory cells (e.g.,131 or 133) in the memory device 130 at a plurality of test voltages(e.g., V_(A), V_(B), V_(C), V_(D), and V_(E)).

A read manager 113 determines bit counts (e.g., C_(A), C_(B), C_(C),C_(D), and C_(E)) at the test voltages (e.g., V_(A), V_(B), V_(C),V_(D), and V_(E)) respectively. Each bit count (e.g., C_(A)) at a testvoltage (e.g., V_(A)) identifies a number of memory cells in the group(e.g., 131 or 133) that, when read at the test voltage (e.g., V_(A)),provide a predetermined bit value (e.g., 0, or 1).

The read manager 113 computes count differences (e.g., D_(A), D_(B),D_(C), and D_(D)) in the bit counts for pairs of adjacent voltages inthe test voltages. Each count difference (e.g., D_(A)) of a voltageinterval between a pair of adjacent voltages (e.g., V_(A) and V_(B)) inthe test voltages is a difference between bit counts (e.g., D_(A) andD_(B)) of the pair of adjacent voltages.

The read manager 113 identifies a situation in which, among the countdifferences (e.g., D_(A), D_(B), D_(C), and D_(D)), a first countdifference (e.g., D_(A)) is no greater than at least two of the countdifferences (e.g., D_(B) and D_(C)), and the first count difference(e.g., D_(A)) has a voltage interval (e.g., V_(A) to V_(B)) that is notbetween two voltage intervals (e.g., V_(B) to V_(C), and V_(C) to V_(D))of the at least two of the count differences (e.g., D_(B) and D_(C)).

In response to such a situation, the read manager 113 determines alocation of an optimized read voltage V_(O) in the voltage interval(e.g., V_(A) to V_(B)) of the first count difference (e.g., D_(A)),based on a ratio between the first count difference (e.g., D_(A)) and asecond count difference (e.g., D_(B)), where the second count difference(e.g., D_(B)) has a voltage interval (e.g., V_(B) to V_(C)) that isclosest to the voltage interval (e.g., V_(A) to V_(B)) of the firstcount difference (e.g., D_(A)), in the at least two of the countdifferences and/or in the count differences.

For example, the location of the optimized read voltage V_(O) can bedetermined based on mapping a logarithmic scale of the ratio between thefirst count difference (e.g., D_(A)) and the second count difference(e.g., D_(B)) to a linear distribution for the location (e.g., V_(A))and the test voltage (e.g., V_(B)) of the optimized read voltage V_(O)in the voltage interval (e.g., V_(A) to V_(B)) of the first countdifference (e.g., D_(A)).

For example, the location of an optimized read voltage V_(O) within thevoltage interval (e.g., V_(A) to V_(B)) of the first count difference(e.g., D_(A)) can be determined without performing floating point numberoperations.

For example, the read manager 113 can generate a plurality of scaledversions of at least one of the first count difference (e.g., D_(A)) andthe second count difference (e.g., D_(B)). The read manager 113 candetermine the location of the optimized read voltage V_(O) based oncomparison performed based on the scaled versions.

For example, the scaled versions can be generated by a shiftingoperation. For example, shifting bit-wise a number to the left by onebit can scale the number up by a factor of two; and shifting bit-wisethe number to the right by one bit can scale the number down by a factorof two. Thus, the scaled versions can be scaled by factors of two to thepower of predetermined numbers without performing floating point numberoperations.

For example, the scaled versions can be generated by repeatedly scalingby a factor of two; and the determining of the location of the optimizedread voltage can be made via comparing a non-scaled one of the firstcount difference (e.g., D_(A)) and the second count difference (e.g.,D_(B)) to the scaled versions one after another.

For example, the determining of the location of the optimized readvoltage V_(O) can include: set the location initially at one of testvoltages (e.g., V_(A) and V_(B)) corresponding to the voltage intervalof the first count difference (e.g., D_(A)); and adjust the location bya predetermined amount in response to a determination that apredetermined relation is satisfied between: the non-scaled one of thefirst count difference (e.g., D_(A)) and the second count difference(e.g., D_(B)), and a scaled version in the plurality of scaled versions.

For example, the read manager 113 initially sets the location at a testvoltage V_(B) that separates the voltage interval V_(A) to V_(B) of thefirst count difference D_(A) and the voltage interval V_(B) to V_(C) ofthe second count difference D_(B). The read manager 113 compares thefirst count difference D_(A) and the second count difference D_(B).

In response to a determination that the first count difference D_(A) issmaller than the second count difference D_(B), the read manager 113moves the location away from the voltage interval V_(B) to V_(C) of thesecond count difference D_(B) by a predetermined amount (e.g., one fifthof the voltage interval V_(A) to V_(B)).

The read manager 113 scales the second count difference D_(B) down by afactor of two to generate a scaled version of the second countdifference (e.g., D_(B)/2), compares the first count difference D_(A)and the scale version of the second count difference (e.g., D_(B)/2),and in response to a determination that the first count difference D_(A)is smaller than the scaled version of the second count difference (e.g.,D_(B)/2), moves the location further away from the voltage intervalV_(B) to V_(C) of the second count difference D_(B) by the predeterminedamount (e.g., one fifth of the voltage interval V_(A) to V_(B)).

The comparison between D_(A) and D_(B)/2 is equivalent to comparingD_(A)/D_(B) and ½, which can be performed alternatively by comparingD_(A)*2 and D_(B).

Subsequently, the read manager 113 can further scale D_(B)/2 down by afactor of two to generate a further scaled version of the second countdifference (e.g., D_(B)/4), and compare the first count difference D_(A)and the further scale version of the second count difference (e.g.,D_(B)/4). In response to a determination that the first count differenceD_(A) is smaller than the further scaled version of the second countdifference (e.g., D_(B)/4), the read manager 113 further moves thelocation even further away from the voltage interval V_(B) to V_(C) ofthe second count difference D_(B) by the predetermined amount (e.g., onefifth of the voltage interval V_(A) to V_(B)).

The comparison between D_(A) and D_(B)/2 is equivalent to comparingD_(A)/D_(B) and ¼, which can be performed alternatively by comparingD_(A)*2 and D_(B)/2, or by comparing D_(A)*4 and D_(B).

In one embodiment, a method is implemented to determine whether anoptimized read voltage for reading a group of memory cells is located ina correct voltage range. The method can be performed by processing logicthat can include hardware (e.g., processing device, circuitry, dedicatedlogic, programmable logic, microcode, hardware of a device, integratedcircuit, etc.), software/firmware (e.g., instructions run or executed ona processing device), or a combination thereof. In some embodiments, themethod of FIG. 8 is performed at least in part by the controller 115 ofFIG. 1 , or processing logic in the memory device 130 of FIG. 2 .Although shown in a particular sequence or order, unless otherwisespecified, the order of the processes can be modified. Thus, theillustrated embodiments should be understood only as examples, and theillustrated processes can be performed in a different order, and someprocesses can be performed in parallel. Additionally, one or moreprocesses can be omitted in various embodiments. Thus, not all processesare required in every embodiment. Other process flows are possible. Forexample, the method can be implemented in a computing system of FIG. 1with a memory device of FIG. 2 and signal noise characteristicsillustrated in FIG. 3 with some of the operations illustrated in FIGS.4-6 .

In the method, a memory device 130 programs a group of memory cells(e.g., 131 or 133) to store multiple bits per memory cell. The group ofmemory cells has multiple pages corresponding to the multiple bits permemory cell respectively.

For example, the group of memory cells (e.g., 131 or 133) can beprogrammed in a QLC mode; the collection the first bit in each memorycell in the group (e.g., 131 or 133) forms a lower page; the collectionof the second bit in each memory cell in the group (e.g., 131 or 133)forms an upper page; the collection of the third bit in each memory cellin the group (e.g., 131 or 133) forms an extra page; and the collectionof the fourth bit in each memory cell in the group (e.g., 131 or 133)forms a top page.

The read manager 113 determines a plurality of read voltages (e.g.,V_(P)) of the group of memory cells.

The memory device 130 reads the multiple pages using the plurality ofread voltages (e.g., V_(P)).

The read manager 113 determines, for each respective page in themultiple pages, a count of first memory cells in the respective page,where each of the first memory cells has a threshold voltage higher thanthe highest read voltage, among the plurality of read voltages, used toread the respective page.

The read manager 113 compares the count of the first memory cells with apredetermined range of a fraction of memory cells in the respective pageto evaluate the plurality of read voltages.

For example, as illustrated in FIG. 3 , each respective read voltageV_(O) in the plurality of read voltages can be determined by the groupof memory cells at a plurality of test voltages V_(A), V_(B), V_(C),V_(D), and V_(E) that are evenly distributed in a test voltage rangefrom V_(A) to V_(B). Bit counts C_(A), C_(B), C_(C), C_(D), and C_(E)can be determined at the test voltages V_(A), V_(B), V_(C), V_(D), andV_(E) respectively. Then, count differences D_(A), D_(B), D_(C), andD_(D) can be determined for the test voltage intervals from V_(A) toV_(B), from V_(B) to V_(C), from V_(C) to V_(D), and from V_(D) to V_(E)respectively. The respective read voltage V_(O) can be calculated basedon the count differences D_(A), D_(B), D_(C), and D_(D) and the length Gof the test voltage intervals.

For example, as illustrated in FIG. 5 , in response to a determinationthat a first count difference D_(B) on a first test voltage intervalV_(B) to V_(C) is no greater than a second count difference D_(A) on asecond test voltage interval V_(A) to V_(B) that is lower than the firsttest voltage interval V_(B) to V_(C) and no greater than a third countdifference D_(C) on a third test voltage interval V_(C) to V_(D) that ishigher than the first test voltage interval V_(B) to V_(C), thedetermining of the respective read voltage can be based on a ratiobetween: an increase (D_(A)−D_(B)) from the first count difference D_(B)to the second count difference D_(A); and an increase (D_(C)−D_(B)) fromthe first count difference D_(B) to the third count difference D_(C).

For example, as illustrated in FIG. 6 , in response to a determinationthat a first count difference D_(A) on a first test voltage intervalV_(A) to V_(B) is no greater than at least two count differences D_(B)and D_(C) on test voltage intervals that are all lower, or all higher,than the first test voltage interval V_(A) to V_(B), the determining ofthe respective read voltage can be based on a ratio between: the firstcount difference D_(A), and a second count difference D_(B) on a testvoltage interval V_(B) to V_(C) that is closest to the first testvoltage interval V_(A) to V_(B) among the at least two countdifferences.

Test voltage ranges for determination of the plurality of read voltages(e.g., from V_(A) to V_(E) for V_(D)) do not overlap with each other.The collection of the test voltage ranges covers a small portion of thevoltage span where optimized read voltages can be in. When a testvoltage range (e.g., V_(A) to V_(E)) is at a wrong location, the readvoltage calibrated/optimized based on the test voltage range is in awrong voltage range. In response to a determination that the count ofthe first memory cells is outside of the predetermined range of thefraction, the read manager can determine that at least one of the testvoltage ranges (e.g., V_(A) to V_(E)) is incorrectly positioned. Inresponse, the memory device 130 can initiate a search for an improvedtest voltage range for the calibration of the read voltage, and/orprovide a response to the controller 115 of the memory sub-system 110 tosuggest a search for an improved test voltage range for read voltagecalibration/optimization.

For example, when the group of memory cells is programmed in aquad-level cell (QLC) mode, the predetermined range of the fraction canbe based on the type of the page. For example, when the respective pageis a top page, the predetermined range of the fraction is between 1/32and 3/32; when the respective page is an extra page, the predeterminedrange of the fraction is between 3/32 and 5/32; when the respective pageis an upper page, the predetermined range of the fraction is between7/32 and 9/32; and when the respective page is a lower extra page, thepredetermined range of the fraction is between 15/32 and 17/32.

FIG. 7 illustrates responses to read operations according to oneembodiment. For example, the responses illustrated in FIG. 7 can beimplemented in a memory sub-system 110 of FIG. 1 having an integratedcircuit memory device 130 of FIG. 2 , using the signal and noisecharacteristics 139 measured according to FIG. 3 .

In FIG. 7 , in response to a read command 169 identifying a group ofmemory cells (e.g., 131 or 133) using an address 135, a calibrationcircuit 145 of a memory device 130 measures signal and noisecharacteristics 139 (e.g., in a way illustrated in FIG. 3 ).

The calibration circuit 145 uses the signal and noise characteristics139 to compute optimized read voltage(s) 151 (e.g., in a way illustratedin FIGS. 4-6 ). The optimized read voltage(s) 151 can be used to read161 hard bit data 177 from the group of memory cells (e.g., 131 or 133).

A read manager 113 of the memory device 130, and/or a read manager 113of the controller 115 of the memory sub-system 110, can have an actionselector 163. The action selector 163 uses the signal and noisecharacteristics 139 to evaluate the quality of the hard bit data 177before, or concurrently with, the reading 161 of the hard bit data 177.

Based on the evaluated quality of the hard bit data 177, the actionselector 163 can suggest an action such as decoding the hard bit data177 without soft bit data 173, obtaining soft bit data, re-calibratingthe optimized read voltage, read retry, etc.

For example, the bit error rate of the hard bit data 177 can beclassified by using the signal and noise characteristics 139 in apredictive model, or estimated by using the signal and noisecharacteristics 139 in an empirical formula. When the bit error rate isclassified as low (or has an estimate that is below a threshold), theaction selector 163 can suggest decoding the hard bit data 177 using alow power decoder 179 that does not use soft bit data 173.

Optionally, the low power decoder 179 is implemented in the memorydevice 130. The memory device 130 can provide a signal to the controller115 of the memory sub-system 110 to extend a response time period forthe read command 169 for the completion of the decoding operation by thelow power decoder 179. Subsequently, the memory device 130 can provide,as a response to the read command 169, the output from the low powerdecoder 179, instead of the hard bit data 177.

Alternatively, the low power decoder 179 is implemented in thecontroller 115 of the memory sub-system 110; and the memory device 130provides the hard bit data 177 as a response to the read command 169.

The optimized read voltage calculated using the techniques of FIGS. 4-6can be classified as optimal or sub-optimal.

For example, when the optimized read voltage is found based on a countdifference (e.g., D_(B)) that is between two larger neighbors (e.g.,D_(A) and D_(C)), the technique of FIG. 5 is used to calculate anoptimal estimate of the read voltage V_(O).

For example, when the optimized read voltage is found without a countdifference (e.g., D_(A)) that is between two larger neighbors, thetechnique of FIG. 6 is used to calculate a sub-optimal estimate of theread voltage V_(O).

When the bit error rate is classified as high (or has an estimate thatis above a threshold) but the optimized read voltage is optimal, theaction selector 163 can suggest obtaining soft bit data 173. Thus, thememory device 130 can further read 171 soft bit data 173 proactively andprovide the soft bit data 173 and the hard bit data 177 for decodingusing a high power decoder 175. The high power decoder 175 consumes moreenergy than the low power decoder but has a better capability torecovery from errors in decoding. For example, the high power decoder175 can be implemented in the controller 115 of the memory sub-system110. Alternatively, the high power decoder 175 can be implemented in thememory device 130.

When the bit error rate is classified as high (or has an estimate thatis above a threshold) and the optimized read voltage is sub-optimal, theaction selector 163 can suggest re-calibrating the optimized readvoltages (151) for a calibrated read voltage that is optimal. Forexample, when optimized voltage is determined to be within the voltagerange V_(A) and V_(B), the memory device 130 can adjust the testvoltages to capture the optimized voltage V_(O) between two largerneighbors. For example, the subsequent calibration can be performed bysetting V_(C) at the current sub-optimal estimate of V_(O).

Optionally, the memory device 130 can suggest a read retry to thecontroller 115 of the memory sub-system 110 and proactively start theread retry before the controller 115 of the memory sub-system 110 issuesa command for read retry. The proactive action can reduce the time tocomplete the read retry.

For example, when an optimized/calibrated read voltage is found to bebased on a wrong test voltage range, the action selector 163 can suggestread retry to the controller 115 of the memory sub-system 110.

In some implementations, when the memory device 130 determines, based onthe signal and noise characteristics 139, that it is unlikely to besuccessful in decoding the hard bit data 177 with the soft bit data 173using a set of read voltages that can be calibrated/determined by thecalibration circuit 145, the action selector 163 can suggest a RAINoperation to the controller 115 of the memory sub-system 110. A RAINdecoder 167 implemented in the controller 115 of the memory sub-system110 can use redundant information stored in other memory devices torecover the data of the group of the memory cells (e.g., 131 or 133).

FIG. 8 shows a method of intelligent proactive responses to operationsof reading data from memory cells according to one embodiment. Themethod of FIG. 8 can be performed by processing logic that can includehardware (e.g., processing device, circuitry, dedicated logic,programmable logic, microcode, hardware of a device, integrated circuit,etc.), software/firmware (e.g., instructions run or executed on aprocessing device), or a combination thereof. In some embodiments, themethod of FIG. 8 is performed at least in part by the controller 115 ofFIG. 1 , or processing logic in the memory device 130 of FIG. 2 .Although shown in a particular sequence or order, unless otherwisespecified, the order of the processes can be modified. Thus, theillustrated embodiments should be understood only as examples, and theillustrated processes can be performed in a different order, and someprocesses can be performed in parallel. Additionally, one or moreprocesses can be omitted in various embodiments. Thus, not all processesare required in every embodiment. Other process flows are possible.

For example, the method of FIG. 8 can be implemented in a computingsystem of FIG. 1 with a memory device of FIG. 2 and signal noisecharacteristics illustrated in FIG. 3 with some of the operationsillustrated in FIGS. 4-6 .

At block 321, in response to a command identifying a group of memorycells (e.g., 131 or 133) in a memory device 130, a calibration circuit145 of a memory device 130 measures signal and noise characteristics 139of the group of memory cells (e.g., 131 or 133).

At block 323, a read manager 113 determines a read voltage 151 based onthe signal and noise characteristics 139.

At block 325, the read manager 113 evaluates the quality of data (e.g.,177 and/or 173) retrievable using the read voltage 151 from the group ofmemory cells (e.g., 131 or 133).

At block 327, an action selector 163 identifies an action based on thequality.

At block 329, the memory device 130 provides, responsive to the command,a response indicating the action.

At block 331, the memory device 130 initiates proactively the actionbefore a subsequent command, following the response, is received in thememory device 130.

For example, to evaluate the quality of the data (e.g., 177 and/or 173),the memory device 130 determines whether an error rate of the data is ina low error rate category and a high error rate category. Further, thememory device 130 determines whether the read voltage 151 is in optimalor sub-optimal, and/or whether the read voltage 151 is in a wrongvoltage range.

For example, the memory sub-system 110 can have a low power decoder 179implemented in the memory device 130 and a high power decoder 175implemented in the controller 115 of the memory sub-system 110.

When the error rate is low, the action can be decoding, using the lowpower decoder 179, hard bit data 177 retrieved by reading the group ofmemory cells at the read voltage, without reading the soft bit data 173.

When the error rate is high but the read voltage 151 is optimal, theaction can be reading soft bit data 173 from the group of memory cells(e.g., 131 or 133) at voltages (e.g., 181 and 182) having predeterminedoffsets (e.g., 183 and 184) from the read voltage 151 such that the highpower decoder 175 can be used to decode the hard bit data 177 with thesoft bit data 173.

When the error rate is high, and the read voltage 151 is sub-optimal butnot in a wrong voltage range, the action can be re-calibration of theread voltage.

When the read voltage 151 is determined to be in a wrong voltage range,the action can be retrying read and/or searching for a voltage range tocalibrate the read voltage.

A non-transitory computer storage medium can be used to storeinstructions of the firmware of a memory sub-system (e.g., 110). Whenthe instructions are executed by the controller 115 and/or theprocessing device 117, the instructions cause the controller 115, theprocessing device 117, and/or a separate hardware module to perform themethods discussed above.

FIG. 9 illustrates an example machine of a computer system 400 withinwhich a set of instructions, for causing the machine to perform any oneor more of the methodologies discussed herein, can be executed. In someembodiments, the computer system 400 can correspond to a host system(e.g., the host system 120 of FIG. 1 ) that includes, is coupled to, orutilizes a memory sub-system (e.g., the memory sub-system 110 of FIG. 1) or can be used to perform the operations of a read manager 113 (e.g.,to execute instructions to perform operations corresponding to the readmanager 113 described with reference to FIGS. 1-8 ). In alternativeembodiments, the machine can be connected (e.g., networked) to othermachines in a LAN, an intranet, an extranet, and/or the Internet. Themachine can operate in the capacity of a server or a client machine inclient-server network environment, as a peer machine in a peer-to-peer(or distributed) network environment, or as a server or a client machinein a cloud computing infrastructure or environment.

The machine can be a personal computer (PC), a tablet PC, a set-top box(STB), a Personal Digital Assistant (PDA), a cellular telephone, a webappliance, a server, a network router, a switch or bridge, or anymachine capable of executing a set of instructions (sequential orotherwise) that specify actions to be taken by that machine. Further,while a single machine is illustrated, the term “machine” shall also betaken to include any collection of machines that individually or jointlyexecute a set (or multiple sets) of instructions to perform any one ormore of the methodologies discussed herein.

The example computer system 400 includes a processing device 402, a mainmemory 404 (e.g., read-only memory (ROM), flash memory, dynamic randomaccess memory (DRAM) such as synchronous DRAM (SDRAM) or Rambus DRAM(RDRAM), static random access memory (SRAM), etc.), and a data storagesystem 418, which communicate with each other via a bus 430 (which caninclude multiple buses).

Processing device 402 represents one or more general-purpose processingdevices such as a microprocessor, a central processing unit, or thelike. More particularly, the processing device can be a complexinstruction set computing (CISC) microprocessor, reduced instruction setcomputing (RISC) microprocessor, very long instruction word (VLIW)microprocessor, or a processor implementing other instruction sets, orprocessors implementing a combination of instruction sets. Processingdevice 402 can also be one or more special-purpose processing devicessuch as an application specific integrated circuit (ASIC), a fieldprogrammable gate array (FPGA), a digital signal processor (DSP),network processor, or the like. The processing device 402 is configuredto execute instructions 426 for performing the operations and stepsdiscussed herein. The computer system 400 can further include a networkinterface device 408 to communicate over the network 420.

The data storage system 418 can include a machine-readable storagemedium 424 (also known as a computer-readable medium) on which is storedone or more sets of instructions 426 or software embodying any one ormore of the methodologies or functions described herein. Theinstructions 426 can also reside, completely or at least partially,within the main memory 404 and/or within the processing device 402during execution thereof by the computer system 400, the main memory 404and the processing device 402 also constituting machine-readable storagemedia. The machine-readable storage medium 424, data storage system 418,and/or main memory 404 can correspond to the memory sub-system 110 ofFIG. 1 .

In one embodiment, the instructions 426 include instructions toimplement functionality corresponding to a read manager 113 (e.g., theread manager 113 described with reference to FIGS. 1-8 ). While themachine-readable storage medium 424 is shown in an example embodiment tobe a single medium, the term “machine-readable storage medium” should betaken to include a single medium or multiple media that store the one ormore sets of instructions. The term “machine-readable storage medium”shall also be taken to include any medium that is capable of storing orencoding a set of instructions for execution by the machine and thatcause the machine to perform any one or more of the methodologies of thepresent disclosure. The term “machine-readable storage medium” shallaccordingly be taken to include, but not be limited to, solid-statememories, optical media, and magnetic media.

Some portions of the preceding detailed descriptions have been presentedin terms of algorithms and symbolic representations of operations ondata bits within a computer memory. These algorithmic descriptions andrepresentations are the ways used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities. Usually, though not necessarily,these quantities take the form of electrical or magnetic signals capableof being stored, combined, compared, and otherwise manipulated. It hasproven convenient at times, principally for reasons of common usage, torefer to these signals as bits, values, elements, symbols, characters,terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. The presentdisclosure can refer to the action and processes of a computer system,or similar electronic computing device, that manipulates and transformsdata represented as physical (electronic) quantities within the computersystem's registers and memories into other data similarly represented asphysical quantities within the computer system memories or registers orother such information storage systems.

The present disclosure also relates to an apparatus for performing theoperations herein. This apparatus can be specially constructed for theintended purposes, or it can include a general purpose computerselectively activated or reconfigured by a computer program stored inthe computer. Such a computer program can be stored in a computerreadable storage medium, such as, but not limited to, any type of diskincluding floppy disks, optical disks, CD-ROMs, and magnetic-opticaldisks, read-only memories (ROMs), random access memories (RAMs), EPROMs,EEPROMs, magnetic or optical cards, or any type of media suitable forstoring electronic instructions, each coupled to a computer system bus.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general purposesystems can be used with programs in accordance with the teachingsherein, or it can prove convenient to construct a more specializedapparatus to perform the method. The structure for a variety of thesesystems will appear as set forth in the description below. In addition,the present disclosure is not described with reference to any particularprogramming language. It will be appreciated that a variety ofprogramming languages can be used to implement the teachings of thedisclosure as described herein.

The present disclosure can be provided as a computer program product, orsoftware, that can include a machine-readable medium having storedthereon instructions, which can be used to program a computer system (orother electronic devices) to perform a process according to the presentdisclosure. A machine-readable medium includes any mechanism for storinginformation in a form readable by a machine (e.g., a computer). In someembodiments, a machine-readable (e.g., computer-readable) mediumincludes a machine (e.g., a computer) readable storage medium such as aread only memory (“ROM”), random access memory (“RAM”), magnetic diskstorage media, optical storage media, flash memory components, etc.

In this description, various functions and operations are described asbeing performed by or caused by computer instructions to simplifydescription. However, those skilled in the art will recognize what ismeant by such expressions is that the functions result from execution ofthe computer instructions by one or more controllers or processors, suchas a microprocessor. Alternatively, or in combination, the functions andoperations can be implemented using special purpose circuitry, with orwithout software instructions, such as using Application-SpecificIntegrated Circuit (ASIC) or Field-Programmable Gate Array (FPGA).Embodiments can be implemented using hardwired circuitry withoutsoftware instructions, or in combination with software instructions.Thus, the techniques are limited neither to any specific combination ofhardware circuitry and software, nor to any particular source for theinstructions executed by the data processing system.

In the foregoing specification, embodiments of the disclosure have beendescribed with reference to specific example embodiments thereof. Itwill be evident that various modifications can be made thereto withoutdeparting from the broader spirit and scope of embodiments of thedisclosure as set forth in the following claims. The specification anddrawings are, accordingly, to be regarded in an illustrative senserather than a restrictive sense.

What is claimed is:
 1. A device, comprising: memory cells; and a logiccircuit configured to, in response to receiving a first command to readthe memory cells: determine an operation based on quality of first dataretrievable from the memory cells; and initiate the operation before thedevice receives a subsequent second command following a response to thefirst command.
 2. The device of claim 1, comprising: a communicationinterface, wherein the device is configured to receive the subsequentsecond command via the communication interface and provide the responsevia the communication interface; and wherein the operation is requiredin execution of the subsequent second command.
 3. The device of claim 2,further comprising: an integrated circuit package enclosing the device.4. The device of claim 2, wherein the logic circuit is configured todetermine the quality based on second data representative of signal andnoise characteristics of the memory cells.
 5. The device of claim 4,wherein to measure the signal and noise characteristics, the logiccircuit is configured to: read the memory cells at a plurality of testvoltages; and determine a plurality of bit counts at the plurality oftest voltages respectively, wherein each bit count at a respective testvoltage represents, among the memory cells, a count of first memorycells that, when read at the respective test voltage, have apredetermined state.
 6. The device of claim 5, wherein the logic circuitis configured to classify a bit error rate based on the second datarepresentative of the signal and noise characteristics of the memorycells.
 7. The device of claim 6, further comprising: a predictive modelconfigured to predict the bit error rate based on the second datarepresentative of the signal and noise characteristics.
 8. The device ofclaim 6, wherein the logic circuit is configured to determine theoperation for initiation before the subsequent second command inresponse to the bit error rate being classified into a predeterminedcategory.
 9. The device of claim 8, wherein the logic circuit is furtherconfigured to calculate a read voltage from the second datarepresentative of the signal and noise characteristics.
 10. The deviceof claim 9, wherein the operation includes decoding the first dataretrieved from the memory cells using the read voltage.
 11. The deviceof claim 9, wherein the operation includes re-calibrating the readvoltage.
 12. The device of claim 9, wherein the operation includes readretry.
 13. The device of claim 9, wherein the operation includessearching for a read voltage to improve the quality.
 14. A method,comprising: receiving, in a device having memory cells and a logiccircuit, a first command to read the memory cells; and in response tothe first command, identifying, by the logic circuit, an operation basedon quality of first data retrievable from the memory cells without usingthe first data retrieved in response to the first command; andinitiating the operation before the device receives a subsequent secondcommand following a response to the first command.
 15. The method ofclaim 14, wherein the device is enclosed within an integrated circuitpackage and has a communication interface to receive the subsequentsecond command and to provide the response; and the operation isidentified to be required in execution of the subsequent second command.16. The method of claim 15, further comprising: measuring signal andnoise characteristics based on: reading the memory cells at a pluralityof test voltages; and determining a plurality of bit counts at theplurality of test voltages respectively, wherein each bit count at arespective test voltage represents, among the memory cells, a count offirst memory cells that, when read at the respective test voltage, havea predetermined state; and determining, by the logic circuit, thequality based on second data representative of the signal and noisecharacteristics of the memory cells.
 17. The method of claim 16, furthercomprising: calculating a read voltage from the second datarepresentative of the signal and noise characteristics; wherein theoperation includes decoding the first data retrieved from the memorycells using the read voltage, re-calibrating the read voltage, readretry, or searching for a read voltage to improve the quality, or anycombination thereof.
 18. A memory sub-system, comprising: a processingdevice; and a device enclosed in an integrated circuit package andcomprising: memory cells; a communication interface configured toreceive, from the processing device, a first command to read the memorycells; and a logic circuit configured to, in response to the firstcommand: identify an operation based on quality of first dataretrievable from the memory cells, before decoding the first data;provide, via the communication interface, a response to the firstcommand; and initiate the operation before the device receives, in thecommunication interface, a subsequent second command following theresponse to the first command.
 19. The memory sub-system of claim 18,wherein the logic circuit is further configured to determine the qualitybased on a plurality of bit counts at a plurality of test voltagesrespectively, wherein each bit count at a respective test voltagerepresents, among the memory cells, a count of first memory cells that,when read at the respective test voltage, have a predetermined state.20. The memory sub-system of claim 19, wherein the operation includesdecoding the first data retrieved from the memory cells using a readvoltage calibrated according to the plurality of bit counts,re-calibrating the read voltage, read retry, or searching for a readvoltage to improve the quality, or any combination thereof.